Ron Weiss - US grants

From Retroactivity to Modularity:
Design and Implementation of a Genetic Insulation Device in Yeast
Domitilla Del Vecchio and Ron Weiss

Modularity is an important property of engineered systems, yet it is debatable whether it is a general property of natural bio-molecular systems. Discovering the extent of modularity and understanding its mechanisms is one of the most important open research problems in systems biology. Furthermore, the long term success of synthetic biology critically depends on the ability to implement modular systems in such a way that the properties of individual components do not change unpredictably upon their interconnection. Our proposed research seeks to understand the mechanisms of modularity in regulatory networks using a combined theoretical/experimental effort through the design, implementation, and analysis of a special genetic retroactivity insulation device in yeast. This novel device effectively decouples transcriptional components that would otherwise be highly interlocked by allowing propagation of a regulatory signal in the forward direction while minimizing the undesired phenomena of retroactivity in the reverse direction. Besides providing an important new circuit element for synthetic biologists, the mathematical analysis of an insulating device will lead to an improved understanding of the extent to which modularity is present in regulatory networks in biological systems. The first aim of our research is to show that retroactivity affects regulatory networks without insulation and therefore that modularity is not a natural property of bio-molecular signaling pathways. Second, we will demonstrate a novel insulation device that counteracts retroactivity and allows a circuit to transmit information reliably despite loading from downstream clients. This special circuit will be designed and placed between two connected components to insulate them from retroactivity effects. We will study the device?s performance, ability to regulate many copies of a downstream component, and requirements for correct operation. Third, we will study how well the insulation device is decoupled from the cellular environment. To this end, we will perform system-level analysis and investigate crosstalk between the device and various important cellular processes.
Intellectual Merit: Existing synthetic circuits lack an ability to insulate a driving input signal from retroactivity of the output load, precluding modular composition of complex biocircuits. To address this problem, we propose to construct and characterize a synthetic phosphorylation-based insulation device and instrumentation pathway that will demonstrate a general and modular technique for building sophisticated, large scale biological systems. Our novel technique leverages the integration of special rapid feedback mechanisms into biocircuits in order to create insulation devices, and hence has implications for the design and implementation of many other biological motifs and networks. Our research includes new theoretical and computational analysis of devices with feedback and retroactivity, and these new tools will also be applicable for the study of biological network problems other than retroactivity. This analysis is fundamentally important for tuning and characterizing desired insulation properties while minimizing interference with other cellular processes.
Broader Impact: In synthetic biology, this research will lead to a general understanding of the engineering principles of modularity for bio-molecular systems design. Likewise, in systems biology, this research will address a fundamental question ? To what extent is modularity an inherent property of biological systems? The product of our collaborative efforts will lead to the discovery in natural systems of motifs similar to our insulation device and to the explanation of how modularity is achieved, including insight into when and where natural systems implement modularity and for what purposes. Ultimately, the resulting capability of modular composition to achieve defined engineering goals in biological systems will have tremendous impact on human therapeutics, including regenerative medicine, diabetes, and cancer therapy, as well as in other diverse areas, including biofuel production, environmental remediation, pharmaceutical production, and biosensing applications. This research will contribute to new interdisciplinary courses and will be integrated into a ten week undergraduate synthetic biology Summer program that culminates in an international competition and has a track record of attracting women, under-represented groups, and high schools students to the field.

This NSF award by the Office of Emerging Frontiers in Research and Innovation supports work to understand how dynamic signaling and cell fate decision circuits in individual cells give rise to multicellular system-level behaviors such as developmental pattern formation and how perturbations to these circuits lead to undesirable consequences such as cancer. These questions will be addressed using three integrated methods: (a) Physical devices for tracking stem cell behaviors in a physiologically relevant environment; (b) Mathematical modeling of cell fate decision circuits; and (c) Engineering of synthetic genetic circuits capable of elucidating fundamental design principles.
Intellectual merit
The team will study the role of intercellular Notch signaling in intestinal colon crypt formation and colorectal tumorigenesis. In Goal 1, an integrated platform including in vitro microscale devices and in vivo mouse homing models will be developed to track signaling dynamics and cell fate decisions in a physiologically realistic environment. In Goal 2, signaling dynamics will be characterized to develop mathematical models of pattern formations in colon crypts. These models will help interpret experimental data and predict perturbation results. In Goal 3, synthetic circuits based on Notch signaling and bacterial quorum sensing components will be implemented in colon cancer stem cells to form de novo patterns, which will be tested using the integrated platform from Goal 1.
Broader impact
This work will have three main broader impacts: (a) facilitating biomedical applications, (b) generating broadly useful tools for the exploration of multicellular patterning systems, and (c) direct impacts on science education through specific activities of the principal investigators and other team members.
Facilitating development of therapeutics. The model system, colon cancer stem cells, is resistant to conventional chemotherapies. Developing effective therapeutics that specifically target cancer stem cells without damaging normal stem cells will require a deeper understanding of the underlying circuits that control self-renewal, differentiation and homeostasis in normal and cancer stem cells.
Tools for understanding differentiation and patterning. The physical devices developed here can be further developed into high throughput drug screening platforms; the synthetic constructs can be used to study signaling mechanisms in diverse cell types; and the mouse homing models enable in vivo studies of human colon stem cells and CRC tumorigenesis.
Educational activity. Using materials from the proposed study, the PIs will work together to teach synthetic biology and mathematical modeling for mammalian systems in systems and synthetic biology courses. The materials will also be included in a synthetic biology textbook in development. The proposed project will be used as an example for K-12 outreach lectures. Finally, simplified versions of the physical devices and the mathematical model from this project will be used as teaching tools for the CURIE Academy, which is a one-week summer residential program focused on creating an interactive atmosphere to engage female high school students in engineering.

This technology introduces a new non-invasive, biopsy-adjunct with a MNP platform for targeted tumor delivery. It employs closely sized magnetic nanoparticles (CS-MNP's) for determining their concentration and spatial distribution in tumor vasculatures as measured by magnetic resonance imaging (MRI). The relationship between CS-MNP size and Vibrating Sample Magnetometer (VSM) measurement follows from the physics of superparamagnetism (SP- theory). By correlating particle size and concentration within a tumor and its MRI response, the data can likely deliver tumor characterization without biopsy. This work characterizes CS-MNP's, animal testing and the quantification of the MRI imaging signals. Resulting data supports continued development of CS-MNP-products and new patents. It addresses the production of CS- MNP products for preclinical studies and business development for a very large market requiring more precise, higher margin products. These new products and services represent the future of MNP-based products for imaging and drug delivery. This technology has potential for a new biopsy-adjunct with CS-MNP's for targeted tumor delivery. Measurement of CS-MNP's in the tumor vasculature by MRI may become a major diagnostic tool and use for targeted therapeutics. It may provide for differentiating benign disease from malignancy, insightful surgical planning, and evaluation of chemotherapy and radiation treatment

The MIT Center for Integrative Synthetic Biology will create the therapeutics of the future by integrating systems biology and synthetic biology. Systems biology, synthetic biology, and fundamental research in health-related applications are three major disciplines that have thrived in the last decade. These fields have pushed the frontiers of biomedical science with the development of high-throughput platforms for generating and analyzing systems-level data, unprecedented abilities to engineer biological systems, and significant advances in our understanding of the molecular mechanisms underlying human disease. However, due to the diverse expertise required in each discipline, these efforts have been largely independent from each other. Therefore there is a significant opportunity for the integration of systems biology, synthetic biology, and health-related applications in a single research center. Our core group of researchers at MIT is poised to spearhead efforts to overcome these challenges with the MIT Center for Integrative Synthetic Biology. A key feature of the proposed Center is our focus on interdisciplinary and collaborative research into next-generation therapeutics. Our major disease-related targets revolve around Cancer, Diabetes, and Infectious Diseases. Together systems biology and synthetic biology will contribute to significant advances in health-related applications. By integrating top-down systems views of disease with bottom-up synthetic construction of novel treatments, this community will create new disease therapies with the ability to integrate multiple inputs and deliver specific interventions. Numerous synergies in synthetic biology, systems biology, and health-related applications will be generated by the MIT Center for Integrated Synthetic Biology which would be difficult to achieve via solely independent investigator-led research efforts.

Nontechnical:

This award by the Biomaterials Program in the Division of Materials Research, co-funded by the Division of Chemical, Bioengineering, Environmental, and Transport Systems, to Massachusetts Institute of Technology is for the development of new class of sustainable materials that are grown using bacteria to have enhanced structure and properties. More specifically, this research program proposes to gain unprecedented control and enhancement of the multiscale design of a technologically important living material system, bacterial cellulose, which has great potential for use as textiles, drug delivery devices, tissue engineering scaffolds, and sustainable building components. This research will enable simultaneous tuning of the material (structure and properties) and macroscopic 3D shape of this biopolymer. An interdisciplinary approach will be taken involving synthetic biology and genetic engineering, in-situ extracellular and materials processing, algorithmic design methods from the field of architecture, as well as powerful new additive manufacturing fabrication (3D printing with micron-scale spatial resolution). This study combines three disciplines - synthetic biology, materials science, and architectural design and has a broader impact contribution for all three. For synthetic biology, foundational methodologies are created that could be extended to any biological polymer (e.g. protein block co-polymers, cellulose, amyloids, etc.). For architectural design, the opportunity to apply methods of algorithmic design and additive manufacturing to living matter is novel and opens up new questions about possibilities of design in interaction with biological growth and material formation to produce sustainable and environmentally responsive materials and building components from renewable resources. For materials science, the project suggests systematic study of combination of material structure, properties and morphometry as a way to design materials and further enhance their function and performance with specific functionalization through synthetic gene networks regulated by external stimuli. Participation in these projects will educate students to cross disciplinary boundaries and work across scales of resolution to develop sustainable design manufacturing techniques for microbial production. Additional educational activities for this study include Independent Activity Period (IAP) interdisciplinary class at MIT "Designing Shape, Material, and Life", instruction in the worldwide synthetic biology competition for undergraduate and high school students iGEM (International Genetic Engineering Machine), and science exhibitions, such as MIT Museum and Cambridge Science Fair. Lastly, mentoring of summer students via undergraduate research programs at MIT will be carried out.


Technical:

This research program proposes to control and tune the material (structure and properties) and macroscopic morphometry (3D shape) of a technologically important model system (bacterial cellulose), which has potential for use as textiles, drug delivery devices, tissue engineering scaffolds, and sustainable building components. An interdisciplinary approach is taken involving synthetic biology and genetic engineering, in-situ extracellular and materials processing, algorithmic design methods from the field of architecture, as well as powerful new additive manufacturing fabrication (3D printing with micron-scale spatial resolution). The first aim of this research is to modulate the structure and properties of cellulose as it is synthesized by the bacteria Gluconacetobacter xylinus via the use of UV lithography regulation and synthetic biological networks encoding fusion proteins production. Secondly, the role of the in situ extracellular physicochemical environmental conditions and perturbations on the structure and properties of bacterial cellulose will be investigated. The resulting macromolecular structure and properties of the produced cellulose will be assessed by cross-polarized optical, electron and atomic-force microscopy, X-ray diffraction, nuclear magnetic resonance, Fourier Transform Infrared Spectroscopy, multi-directional mechanical testing, and nanoindentation. Lastly, algorithmic design and 3D printing will be utilized to fabricate increasingly complex macroscopic structures with tunable geometric parameters of molds which are subsequently used to cast polydimethylsiloxane substrates for in situ culture and growth of genetically engineered bacterial cellulose.

Successful computing systems leverage their underlying technologies to solve problems humans simply cannot. Electronic systems harness the power of radio waves and electrons. Mechanical systems use physical force and physical interactions. Biological systems represent a computing paradigm that can harness evolution, adaptation, replication, self-repair, chemistry, and living organisms. Engineered, living biological systems which make decisions, process "data", record events, adapt to their environment, and communicate to one another will deliver exciting new solutions in bio-therapeutics, bio-materials, bio-energy, and bio-remediation. This project will create a quantitative set of freely available design principles, computational tools, mathematical models, physical biological artifacts, educational resources, and outreach activities. Once available, these resources will allow for novel, living biological solutions to be built more quickly, perform better, be more reliable to manufacture, and cost less to produce. This project is unique in that these resources will be explicitly developed to validate key computational concepts to understand how well these concepts can be applied rigorously and repeatedly to biology. This project decomposes these concepts into three areas: Computing Paradigm (digital, analog, memory, and communication), Computing Activity (specification, design, and verification), and Computing Metric (time, space, quality, and complexity). Once complete, this project will provide the most comprehensive, freely available, and computationally relevant set of building blocks to engineer biological systems to date.

By developing the tools, techniques, and materials outlined in this project, this research will fundamentally change the way biological systems are specified, designed, assembled, and tested. Advanced bio-energy, bio-sensing, bio-therapeutics, and bio-materials all will become increasingly viable commercial technologies that can be made better, cheaper, faster, and more safely as a result of this project. The education of an entire new generation of engineers will occur through workshops, coursework, and community engagement activities. This new generation will have access to these approaches which will influence how biological computation is done and how that process is communicated to the community. This project will bring computational questions and methods to the forefront of biotechnology via an interdisciplinary research team focused not on one-off solutions but on foundational computing principles.

Explicitly five unanswered questions will be addressed in this project: (1) What computational models are available to biology, what are their limits, and how do they perform? (2) What communication mechanisms are available to biology, what are their limits, and how do they perform? (3) What are the theoretical and empirical measures of quality, scale, time, and space in biological computing systems? (4) How generalizable are the concepts and "design rules" which can be learned from studying biological systems? (5) How can the results (data and learnings) from biological specification, design, and verification be authoritatively disseminated to the community as design principles and grand challenges? This project addresses these questions with an interdisciplinary team with expertise in theoretical computer science, electronic design automation, bio-physics/chemistry, control theory, and molecular cell biology. For more information visit www.programmingbiology.org.

? DESCRIPTION: Data from patients in diverse cancer types show that increased immune cell infiltration of tumors correlates with improved patient prognosis. It is also clear that a host of immunosuppressive signals are produced by tumors in order to block immune attack, and thus the endogenous immune response is in most cases unable to eliminate established tumors. Recently, efforts to alter the suppressive signaling in tumors through checkpoint blockade drugs that block individual suppressive signaling receptors on T-cells have shown promising clinical results, demonstrating tumor regression through shifting of the tumor microenvironment toward a pro-immunity state. However, it is likely that blocking individual signaling pathways will be insufficient for cancer immunotherapy to reach its full potential, because tumors create a complex network of suppressive signals. Here we propose an approach based on integration of synthetic biology and systems biology to reprogram the suppressive microenvironment in tumors through delivery of sophisticated multi-step genetic circuits to cells in the tumor microenvironment. This strategy will be enabled by the development of self-replicating RNA-based circuits that can (i) identify cell types specifically in the tumor through miRNA expression profiles, (ii) utilize small molecule-regulated induction of new gene expression programs that act in trans on surrounding cells in the tumor, and (iii) alter the function of identified cells in a coordinated fashion to tip the balance within the tumor from immune suppression to immune-mediated tumor destruction. Our specific aims are: (1) We will design a platform for in vivo RNA-based synthetic biology comprising RNA-binding regulatory proteins and small molecule induced protein degradation domains; (2) We will profile the miRNA signatures of 4T1 breast cancer cells and B16F10 melanoma cancer cells and create RNA-encoded multi-input classifier circuits that permit replicon expression only in target cell types, and (3) We will build a series f increasingly sophisticated programmed cancer therapies where cytokine secretion and necroptotic gene expression is restricted to tumor cells and can be precisely regulated by FDA approved small molecules, along with safety switch mechanisms to eliminate replicon-encoded circuits in healthy cells. We will deliver our therapeutic circuits into mouse tumors in vivo and monitor anticancer immune responses, using systems biology principles to analyze the resulting response of multi-cell networks in the tumor. A key goal of these studies will be to design RNA circuits which drive transfected tumor/immune cells to act in trans on surrounding cells in the microenvironment, to achieve a tumor-wide change in the tumor milieu without the need for successful circuit delivery to every cell in the tumor. Results from this project will provide a ne RNA-based toolkit for engineering mammalian cell function, demonstrate the utility of these approaches in vivo, and provide a framework for reprogramming tumors that overcomes limitations of traditional chemotherapy and targeted drug treatments.

Synthetic genetic feedback controller circuits to reprogram cell fate PI: Domitilla Del Vecchio1;4 co-PIs: James J. Collins2;4;5;6, Thorsten Schlaeger7, and Ron Weiss2;3;4 1Department of Mechanical Engineering, MIT; 2Department of Biological Engineering, MIT 3Department of Electrical Engineering and Computer Science, MIT 4Synthetic Biology Center, MIT; 5Broad Institute of MIT & Harvard; 6The Wyss Institute 7 Stem Cell Transplantation Program, Boston Children's Hospital PROJECT SUMMARY The past decade has seen monumental discoveries in the stem cell ?eld, with demonstrations that the fate of a terminally differentiated cell, contrary to what was traditionally believed, could be reverted back to pluripotency or directly converted to other differentiated cell types. All of a sudden, new approaches to regenerative medicine seem within reach: lost or damaged cells could be replaced by patient-speci?c reprogrammed cells, thus providing on- demand, compatible, high-quality cells of any required type. To meet this vision, the scienti?c community has made tremendous efforts toward establishing robust and ef?cient protocols for cell fate reprogramming. These protocols are largely based on a priori ?xed (pre?xed) ectopic overexpression of suitable transcription factors (TFs), with the rationale that this overexpression could trigger transitions among the states of the gene regulatory networks (GRNs) that take part in cell fate determination. Yet, despite a decade of remarkable progress, the ef?ciency of these protocols remains low, the quality of produced cells is often unsatisfactory, and many potentially useful direct cell fate conversions still seem impossible. These issues pose a formidable obstacle to the practical use of both human induced pluripotent stem cells (hiPSCs) and transdifferentiated cells in regenerative medicine. Arguably, our ability to accurately and precisely steer the concentrations of GRNs' TFs within desired ranges is critical to the success of cell fate reprogramming. Unfortunately, current protocols based on pre?xed TFs' overexpression have not demonstrated this critical ability. To address this problem, we propose a completely new approach to cell fate reprogramming in this project: we replace pre?xed overexpression with feedback overexpression of TFs, which we realize with an in vivo synthetic genetic feedback controller circuit. Within this circuit, the overexpression level is not a priori ?xed and is adjusted based on the discrepancy between desired and actual TF's concentrations. It therefore can accurately and precisely control TFs' concentrations to desired values, independent of the endogenous GRN that also regulates these TFs. Our research plan focuses ?rst on hiPSC reprogramming as a test-bed for evaluating the bene?t of our approach and second on directed differentiation of hiPSCs into platelets as a directly clinically relevant application. Speci?cally, in AIM 1, we propose to systematically investigate the ef?cacy of pre?xed overexpression of pluripotency TFs for hiPSC reprogramming. In AIM 2, we propose to construct and test the synthetic genetic feedback controller circuits that implement feedback overexpression of a number of TFs concurrently. In AIM 3, we will leverage the synthetic genetic feedback controller circuits for human hiPSC reprogramming and for directed differentiation of hiPSCs into platelets. This project will result in substantially higher reprogramming ef?ciencies, in cell products that more closely resemble the target cell type, and in the future, in cell conversions that today seem not possible. More broadly, our synthetic genetic feedback controllers will empower scientists and practitioners with a new tool to accurately control the TFs' concentrations of any endogenous GRNs and, in particular, of those GRNs involved in cell fate determination. 1

We propose a new platform that leverages synthetic biology genetic circuits and micro/mesofluidic instrumentation to rapidly advance the field of organoids. Specifically, we will genetically engineer self- organizing tissues from human pluripotent stem cells into co-developed liver and pancreas organoids possessing vascularization and other mature properties, such as adult level albumin production. The application of synthetic biology to organoid development (programmable organoids) provides an exciting new opportunity for engineering and testing of organoids encoding live cell sensors of cell state and for embedding circuits that express cell-type and cell-state specific transcription factors. We will engineer novel microfluidic and mesofluidic platforms to enable low cost and high throughput development and testing of programmable organoids. Our hypothesis is that co-development of hiPSC-derived liver and pancreas provides cell-cell interactions that contribute to vascularization and other important elements in organoid development that will lead to mature organ formation. To test our hypothesis, we will genetically encode live-cell sensors to monitor liver organoid develop, co-develop liver and pancreas organoids, and create genetic circuits that lead to mature organoid formation. We will use synthetic classifier genetic circuits that evaluate changes in cell state in real time, and generate relevant protein outputs for driving differentiation in a cell-type specific manner. This these circuits will enable autonomous generation of new and improved versions of organoids, including mature liver organoids and co-developed liver/pancreas organoids. Determining the precise spatiotemporal nature of cell state transitions and the relevant transcription factors to drive differentiation is not only essential for creating new and effective organoid developmental programs, but will also provide important scientific insights to understanding the fine aspects of differentiation and co-development. Successful achievement of our aims will have a broad impact in the areas of gene therapy, drug testing, and personalized medicine. For example, the ability to co-develop matched organoid systems will enable patient-specific drug development (e.g. for cancer therapy) that is more accurate than expensive and controversial alternatives, such as the use of humanized mice. This work will also support the long-term goal of producing mature organoids and organ systems suitable for transplantation.

People with diseased or defective vital organs often need organ replacement to survive, but the availability of replacement organs is severely restricted by shortages of suitable tissue-matched donors and complexities such as postmortem organ deterioration and immunological rejection. These problems could be overcome by using high fidelity artificially-grown organs, but achieving that goal faces daunting and long-standing scientific and engineering challenges that this project aims to begin to meet. The project will focus on proof-of-concept generation of microscale patterns in a liver organoid to mimic the anatomical structure of lobules arranged in hexagonal patterns. The researchers will use microrobots to dynamically regulate gene expression in 3D vascularized liver organoids to generate the lobule like patterns. The results of this project will define a new area of robot-assisted biological design. This research will result in new biological rules, synthetic biology tools, and microrobotics that can be applied in numerous disciplines. If successful, another broader impact will be the demonstration of a method that could be used to create a new, in vitro, native-like organoid for biological and medical research, opening the door for research into the creation and repair of synthetic human organs. The project includes research training for graduate students and postdoctoral researchers.

Conventional methods of reproducing biological patterns in vitro suffer from multiple limitations. Previous research on pattern formation has largely relied on delivering global stimuli and studying reaction-diffusion mediated patterning of cell fates in the cell culture. Such methods yield only static patterns and give neither precise spatial nor temporal control over gene expression and resulting biological tissue formation. Current tissue engineering capabilities such as 3D printing and optogenetics are also unable to recapitulate the multiscale self-assembled patterns evident in native-like organs. The proposed approach will enable precise control of microrobots to achieve dynamic control over patterning in 3D biological systems, creating a paradigm shift in the field. The proof-of-concept goal is to modulate localized gene expression in engineered 3D tissue constructs to control the emergence of multiscale patterns. Machine learning will be used to derive and characterize desired multiscale patterns, synthetic biology to endow the stem cells with genetic circuits that can differentiate the cells to form desired tissue constructs, and microrobots to alter localized gene expression to form multiscale patterns in tissue constructs. In particular, the researchers will develop and control microrobots capable of sustaining and carrying engineered sender cells, drive the microrobots and associated sender cells within a vascularized 3D liver organoid to specific locations, and use the microrobot controlled sender cells to communicate with endothelial cells, inducing these endothelial cells to secrete Wnt and generate gradients controlling liver lobule zonation. This patterned lobule zonation will regulate the metabolic activity of the liver organoids.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Major advancements in stem cell biology have paved the way for innovation in organoid engineering. Organoids are 3D tissues derived from human induced pluripotent stem cells (hiPSCs) generated by reprogramming patient- speci?c adult cells, such as ?broblasts. While organoids show great promise as testbeds for investigating devel- opmental biology, current methods for organoid production are limited by their reliance on external inputs, such as growth factors and small molecules, which affect cells imprecisely and give rise to immature organoids that do not faithfully recapitulate in vivo physiology and functionality. The resulting organoids are size-constrained, lim- ited to a small set of cell types, and do not generally develop mature tissue that exhibits the functionality of fully developed organs. While we have previously demonstrated genetic programs that enable organoids to generate all requisite cell types in liver, variability in cell ratios remains an open challenge for achieving reproducible, high quality organoids. Further, progress is blocked by the inability to reliably guide multi-lineage speci?cation, the lack of precise timing of multistep differentiation, and the inability to make robust bifurcation decisions that determine the ratios of the resulting cell types. To overcome these obstacles, we will combine synthetic biology, developmental biology, and control theory to design novel open and closed loop genetic controllers that individually guide differentiation from within each cell to form unique new 3D tissue: vascularized pancreatic organoids with de?ned ratios of endocrine and exocrine cells. We will demonstrate how these new organoids can serve as more sophisticated and comprehensive models for investigating developmental biology principles. This work will spearhead a transformation in organoid synthesis by shifting the ?eld from manual addition of inductive chemical signals to cell type conditional, self-timed ectopic expression of transcription factors that induce differentiation. Building upon the premise that 1) gene sensors can detect cell types speci?c to differentiation stages, and 2) at least in certain important cases, regulated expression of lineage-specifying transcription factors can guide differentiation to the next stage, our main hypothesis is that feedback regulation of cell lineage bifurcation decisions can lead to more robust and reproducible sub-population ratios in organoids in comparison to open loop approaches. Our organoids will contain synthetic developmental programs that are self-timed and globally-orchestrated, with cells working together to generate the requisite ratios. We will create a platform for programmed bifurcation decisions that can be used for other differentiation steps in the pancreas, and more broadly to other organoid and tissue types. We will use this platform to perform novel developmental studies to systematically vary the ratio of endocrine to exocrine cells and measure the consequences on exocrine/endocrine cells and their differentiation and function. The ability to precisely vary the ratio while studying gene expression pro?les, the organoid secretome, and its affects on target cells will provide invaluable bene?t in the investigation of pancreas development and dysfunction.

Project Summary Human induced pluripotent stem cell (hiPSC)-derived organoids hold great promise for tissue engineering and personalized drug screening, but obtaining the desired multicellular organization and function from these systems is usually performed in an ad hoc fashion without forward design specification. Recently, we reported successful liver bud formation containing stromal cells, vascular tube-like structures and hematopoiesis-like processes by synthetically inducing diversity in GATA6 expression from a single hiPSC population. This accomplishment suggests that expanding circuit logic operations to artificially control differentiation drivers at particular bifurcations in lineage specification could profoundly impact the complexity and functionality of organoids. In this project, we bring together mathematical modeling, machine learning, optimization, and innovative synthetic biology techniques to elucidate and design fundamental decision and communication rules for guiding cells into complex, heterogeneous tissues. Our overarching hypothesis is that appropriate timing and predictable stochastic control of the expression of intracellular and extracellular factors is critical for redirecting lineage choices in order to elicit desired multicellular organization from a population of differentiating cells. We will develop synthetic tools for sensing differentiation stages of iPSC-derived organoids and construct and characterize a stochastic commitment switch in an inducible reporter system. These tools will be integrated in synthetic gene circuits for engineering emergent multicellular organization through stochastic temporal control of developmental factors. The modular commitment switches developed in this project will be capable of exploring how the degree of subpopulation biasing of cell fate decisions and level of cell fate synchronization at bipotent differentiation stages impacts self-assembly and emergent multicellular organization of an organoid. Our aims - executed through a closed loop of computational and experimental investigations - will shed insight on how generalizable methods of controlled manipulation can elicit desired organoid-level emergent properties.

This project aims at defining a new area of dynamically-controlled, robot-assisted biological design. A convergent research team consisting of experts in microrobotics, machine learning, and synthetic biology will focus on developing a radically new approach towards analyzing and replicating intricate cellular patterning in mammalian tissues. Not only will this research result in new biological rules, synthetic biology tools, and microrobotics that can be applied in numerous disciplines, it will also create a new in vitro native-like liver organoid for biological and medical research. The work will open the door for research into the creation and repair of other synthetic human organs.<br/><br/><br/>The research team will generate multiscale, multicellular patterns and synthesize 3D patterns with vasculature to produce native-like organs. To enable mapping of pattern design problems to optimization problems, the researchers will define novel machine learning techniques to classify and quantify cellular patterns. To enable control of bio-compatible robots that guide cell organization and differentiation processes, they plan to develop novel technologies that leverage and far exceed current microrobotics. This will enable insights into fundamental scientific questions about cell differentiation and connections between spatial organization and function.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.